Professional analysts utilize sports videos to investigate the strategies in a game, examine new players, and generate meaningful statistics. In order to analyze the videos, they watch many broadcasts to spot the highlights within a game, which is a time-consuming and costly process. Fortunately, automated sports analytic methods developed in the computer vision field can facilitate sports broadcasts understanding. In recent years, many automated methods have been proposed to help localize the salient actions of a game. They produce statistics of events within a game by either analyzing camera shots or semantic information. Human activity localization in sports videos is studied in [1]}, [2]}, [3]}, [4]}, salient game actions are identified in [5]}, [6]}, automatic game highlights identification and summarization are performed in [7]}, [8]}, [9]}, [10]}, [11]}. Moreover, action spotting, which is the task of temporal localization of human-induced events, has been popular in soccer game broadcasts [12]}, [13]} and some methods aimed to automatically detect goals, penalties, corner kicks, and card events [14]}. Action detection algorithms can inspire many of the tasks mentioned above.
